Your team is developing a web application that will be deployed on Google Kubernetes Engine (GKE). Your CTO expects a successful launch and you need to ensure your application can handle the expected load of tens of thousands of users. You want to test the current deployment to ensure the latency of your application stays below a certain threshold. What should you do?

The most effective way to ensure an application can handle a large user load and meet latency requirements is to simulate that load. A load testing tool can generate the expected concurrent users and requests, allowing you to observe the application's performance under stress and identify bottlenecks. Enabling autoscaling and sending curl requests will only validate autoscaling functionality, not the application's performance under sustained high load. Replicating the application across multiple GKE clusters and using a global HTTP(S) load balancer is a strategy for high availability and global reach, not a method for performance testing a single deployment's latency under expected load. Cloud Debugger is for debugging code in development, not for assessing production-like performance or latency under load.